LLM vs Traditional ML: Customer Support Triage Agent

Description and use

LLM on Oxlo.ai outperforms traditional ML in ticket classification and response drafting, consolidating multiple tasks into a single call.

Problem

Teams need a system to classify support tickets, detect urgency, and draft initial replies, but traditional ML struggles with nuance and context.

Outcome

The LLM on Oxlo.ai outperformed the traditional ML baseline by accurately detecting urgency, providing sentiment analysis, and drafting a helpful reply in a single request.

Workflow

• 1. Prepare labeled ticket data and train a logistic regression baseline with TF‑IDF.
• 2. Define a system prompt for the LLM that requests JSON output with category, urgency, sentiment, and draft reply.
• 3. Connect to Oxlo.ai using the OpenAI SDK and the llama‑3.3‑70b model.
• 4. Send the same ticket to both the traditional ML model and the LLM.
• 5. Compare outputs: traditional ML gives a label and confidence; LLM returns structured decision and draft reply.
• 6. Run the script, observe the LLM’s superior handling of context and urgency.
• 7. Optionally swap the model or add function calling to open tickets directly in CRM.

Evidence

The LLM on Oxlo.ai returned a structured decision, detected urgency from context, and drafted a reply in one request.

Source